Parameter identification for two-dimensional orthotropic material bodies by the boundary element method

An identification algorithm based on an isoparametric graded finite element model is developed to identify the material parameters of the plane structure of functionally graded materials (FGMs). The material parameter identification problem is formulated as the problem of minimizing the objective function, which is defined as a square sum of differences between measured displacement and calculated displacement by the isoparametric graded finite element approach. The minimization problem is solved by using the Levenberg-Marquardt method, in which the sensitivity calculation is based on the differentiation of the governing equations of the isoparametric graded finite element model. The validity of this algorithm is illustrated by some numerical experiments. The numerical results reveal that the proposed algorithm not only has high accuracy and stable convergence, but is also robust to the effects of measured displacement noise.

The traditional linear generation fuzzy fractional differential equation parameter identification algorithm lacks the update of parameter identification process, has large amount of calculation, slow convergence speed of parameter identification and strong dependence on initial values. In this paper, a new intelligent recognition algorithm for linearly generated fuzzy fractional differential equations is proposed. The parameters of the equation are re expressed by the constant level set. The piecewise constant level set algorithm based on equation parameter intelligent identification is used to solve the steady-state solution of fractional differential equation, and the non convergence problem caused by too much calculation is solved. A new algorithm scheme for linearly generating fuzzy fractional differential equations is established, the constraints of the level set of the differential equations are calculated, and the updated algorithm for parameter identification of the equation is obtained. The evolutionary algorithm is used to solve the updating algorithm to realize the intelligent identification algorithm of linear fractional fuzzy differential equations. Experimental results show that the algorithm has the advantages of fast convergence speed, high calculation accuracy and low initial value.

Defect identification in laminated composite structures by BEM from incomplete static data

BEM and penalty FEM models for viscous incompressible fluids

This paper concentrates on the design of the optimal measurement placement for the parameter identification of two-dimensional orthotropic composites, which is modeled by the boundary element. From the analysis of the well-posedness of the parameter identification processes using the Levenberg–Marquardt method, a performance indicator and an estimation of the maximum bias of identified parameters are deduced. Based on these results, a method for selecting the optimal measurement placement is proposed. The validity of this method is illustrated by some numerical examples. These examples reveal that the measurement placement has significant influence on identification results. Furthermore, an iterative process of selecting measurement placement is suggested for practical implementation.

The subject of this work is implementation of algorithms for classification and parametric identification of ballistic models based on radar measurements. This can involve different theoretical approaches and technical solutions. The choice of methods is discussed including mathematical models based on differential equations, numerical integration methods and non-linear optimisation techniques. In addition multi-thread programming techniques are applied in order to adapt implementation to modern multi-core processor architectures. Possibility of using specialised open-source libraries is also analysed with regard to technical aspects and taking into account the specificities of a commercial project and licensing conditions.

This study proposes an improved Black-Winged Kite Algorithm (SRQ-BKA) for accurate parameter identification of the photovoltaic (PV) double diode model (DDM). The proposed method integrates three key mechanisms: specular reflection learning (SRL) to improve initial population diversity, preventing premature convergence and enabling a more comprehensive exploration of the solution space for optimal parameters; soft rime search (SRS) to balance global exploration and local exploitation, ensuring efficient identification by dynamically adjusting the search focus; and quadratic interpolation (QI) to accelerate convergence by fine-tuning the search toward optimal parameters, enhancing accuracy and speeding up the identification process. The root mean square error (RMSE) is employed as the objective function to minimize the error between the measured and predicted I-V characteristics of the PV module. Experimental results demonstrate that the SRQ-BKA outperforms other algorithms, achieving a minimum RMSE of 0.00262 A for the DDM and exhibiting strong stability, as evidenced by an average RMSE of 0.00278 A across 1000 runs. The method also demonstrates excellent parameter identification accuracy for both the single diode model (SDM) and triple diode model (TDM), further validating its robustness and practical applicability.

Real‐Time System Identification: An Algorithm for Simultaneous Model Class Selection and Parametric Identification

Modelling and parameter identification of electromechanical systems for energy harvesting and sensing

In this paper one presents an algorithm for a DC motor parameters identification from sample data using the distribution approach. While most of the latest methods used in identification utilize a discrete-time model, the distribution method is an alternative approach to directly identify a continuous-time model from discrete-time data. The relation between the state variables is represented by functionals using techniques from distribution theory. Based on these relations, an algorithm for off-line parameter identification is developed. The method is applied to identify the parametric model of a real experimental platform and of a simulated model.

In the past 20 years, the US Navy has used the physical scale modeling (PSM) technique to design effective cathodic protection (CP) systems for the ships underwater hull, nickel-aluminum-bronze props and other hull components.In more recent years, a number of computational techniques have been devised in an attempt to fulfill this purpose.Physical models have proven highly adept at ICCP design, since modeled information provides a direct relationship to the actual hull and can be scaled up directly because of confidence in the physically measured data.Boundary element (BE) models have been correspondingly devised that mimic actual hull design and even the PSM layout, but because the BE method is a computational methodology, the calculated data requires systematic validation with a physical analog to insure confidence in the control response.BE literature has discussed design issues regarding mesh layout, intrinsic geometric complexities, accuracy of material response input, the predictive engineering design capability for zonal response, and assessment of electric field response.It does not significantly discuss the accuracy of the BE model calculated work predictive design capability, without the need for "tweaking," and ultimately a rigorous validation of both the mesh and resultant system design technique.This paper presents validation requirements, for any BE model, that is inherently robust enough to be used for CP design and control, and a proposed four-point methodology that will allow for the comprehensive validation of the BE model to predict the ICCP control responses and system performance behavior.

In a previous research project [SPR 2418; "Study of the performance of acoustic barriers for Indiana toll roads,"] the influence of environmental factors and of advanced sound barrier concepts was investigated. The presence of temperature gradients over pavements was found to have a strong influence on sound propagation. Refraction of sound waves emitted by tire-road interactions in the vicinity of the ground also affects sound barrier performance. Modified ray tracing model suggested that prevailing winds have an influence on barrier performance at large distances. Randomized edge configurations were found not to improve barrier performance for traffic noise. Random edges simply scatter sound energy without any net noise reduction. Although the edge can be optimized for specific frequency components and locations, it appears that optimization for broadband noise control is difficult. The study also suggested that adding sound absorptive material along the barrier edge could enhance barrier performance. The present study is the continuation of the latter effort to confirm the findings related to the sound absorptive treatment on the barrier through a more rigorous study and to apply the design concept to a realistic situation. A comparison was made between barriers that incorporated sound absorptive treatments and barriers with T-shaped tops. The results confirmed that a sound absorptive treatment near the barrier edge resulted in a performance improvement over corresponding rigid barriers. A design optimization study of the most effective shape of acoustic treatments concluded that a circular shape works best. The performance of two different acoustical materials was also compared. Use of glassfiber resulted in better performance in the high frequency region, while polyolefin foam with closed cells achieved a relatively large insertion loss at low frequencies. Efforts were made to develop a numerical predictive model. The boundary element method was used to model the infinite size surrounding fluid effectively. The disadvantage of the boundary element model is the calculation load associated with the large number of elements required for high frequency analysis. A mesh optimization procedure was successfully implemented in the boundary element model to reduce the calculation time while satisfying the tolerances for analysis accuracy at each analysis frequency. Octave band averaging was also adapted to facilitate the comparison between the numerical results and experimental data. It was found that the results from the boundary element model agree relatively well with the experimental results up to 6300 Hz at selected locations. The insertion loss distribution proved the

Recently, research on new mufflers equipped with connected curved tubes using phase cancellation techniques has been addressed in the industrial field. On the basis of the transfer matrix method and the stiffness matrix method, researchers have explored noise reduction effects within a constrained space. However, the attenuation bands are too narrow and the overall acoustical performance in dealing with broadband noise is thus insufficient. Therefore, in order to broaden the spectrum of the acoustical performance curves at various targeted tones (250, 500, and 900 Hz), a wool optimally lined within the ducts of HQ mufflers is proposed. In this paper, five kinds of HQ mufflers with rectangular sections (muffler A: a double-connected curved tube muffler; muffler B: a double-connected abrupt tube muffler; muffler C: a one-sided triple-connected curved tube muffler; muffler D: a two-sided tripleconnected curved tube muffler; muffler E: a one-sided triple-connected abrupt tube muffler) within a fixed length are assessed. In order to facilitate the assessment of optimal mufflers, a boundary element model (BEM) is adopted as the mathematical objective function. To assess the optimal mufflers, a genetic algorithm (GA) is linked with the BEM. Before the GA operation can be carried out, the accuracy of the mathematical models must be checked using the experimental data. Optimal results reveal that the maximum value of the sound transmission loss (STL) can be optimally improved at the targeted frequencies. Consequently, the optimum algorithm proposed in this study provides an efficient way to find a better silencer for industry.

This paper presents a dynamics identification algorithm based on a disturbance observer for a multi-degrees-of-freedom robot. In the conventional approach, the disturbance observer is used to increase the robustness of the robot. In this case, the estimated disturbance torque is directly fed back to cancel out the disturbance torque imposed on the robot. In this paper, the estimated disturbance torque is analysed from the physical point of view and the robot dynamics are identified by resolving the estimated disturbance torque. First, the basic structure of the disturbance observer is simply introduced from the motion equation of the robot. Second, the parameter identification algorithm based on the disturbance observer is explained. Finally, we show the numerical and experimental results to confirm the validity of the proposed parameter identification algorithm.

A novel identification algorithm is presented in this paper for neuro-fuzzy based single-input-single-output (SISO) Wiener model with colored noises. The independent identical distribution (iid) Gaussian random signals are adopted to identify the Wiener system, leading to the separation of linear part from nonlinear counterpart in the identification problem. Therefore, correlation analysis method can be used for the identification of the linear part. Moreover, least-squares-based parameter identification algorithm that can avoid the impact of colored noise is proposed to identify the static nonlinear part. Lastly, an example is used to verify the effectiveness of the proposed method.